The scalar multiplication of an analog input with PCM-based weights is shown to be comparable to an implementation with 4-bit fixed-point inputs and weights [1]}, and even to an implementation with 8-bit fixed-point inputs and weights with suitable innovations in device design [2]}. To counter the reduced weight precision, one can employ noise during training, so that the model is more robust when performing inference on AIMC tiles [3]}.
An alternative approach is to encode weights using multiple PCM devices [4]}. Despite the reduced precision weights, AIMC implementations were shown to address the inference of MLPs [5]}, [6]}, CNNs [3]}, [5]}, [6]}, RNNs [4]}, [5]}, [6]}, and transformers [13]} with high accuracies.
